Super-resolution upscaling of a single noiseless input image by exploiting multi-scale self-similarities is generally known.
Dictionary-based super-resolution methods (J. Yang, J. Wright, T. Huang, and Y. Ma, Image Super-resolution via Sparse Representation, IEEE Trans, on Image Processing, pp. 2861-2873, vol. 19, issue 11, May 2010) use an example dictionary created from a representative collection of training images. It is therefore necessary to adapt the dictionary to the kind of contents in the image to be super-resolved. Creating a dictionary can take from several minutes to hours, so the classes of images that can be super-resolved must be defined in advance.
In contrast, single-image super-resolution methods (D. Glasner, S. Bagon, and M. Irani, Super-Resolution form a Single Image, ICCV 2009) can exploit multi-scale self-similarities for finding examples at different scales of the image. One important drawback of this approach (and related techniques based on the same principle) is that the combination of overlapping patches obtained from within the image leads to incompatibilities between the lower spectrum of the super-resolved image and the input image. This is solved using iterated back-projection (IBP), which introduces other artifacts, such as ringing. Moreover, this method only considers linear combinations of example patches (weighted average) when reconstructing the super-resolved image.
The inventors' prior work on single-image super-resolution exploiting the cross-scale self-similarity property was able to avoid using IBP by introducing high-frequency examples complementing the interpolation-based up-scaled version of the input image. A drawback is that high-resolution versions of image structures at different scales cannot be gained. Generally, the methods mentioned above provide only partial solutions for generating super-resolved images.